Macrophage membrane (MMs) camouflaged near-infrared (NIR) responsive bone defect area targeting nanocarrier delivery system (BTNDS) for rapid repair: promoting osteogenesis via phototherapy and modulating immunity.

Bone defects remain a significant challenge in clinical orthopedics, but no targeted medication can solve these problems. Inspired by inflammatory targeting properties of macrophages, inflammatory microenvironment of bone defects was exploited to develop a multifunctional nanocarrier capable of targeting bone defects and promoting bone regeneration. The avidin-modified black phosphorus nanosheets (BP-Avidin, BPAvi) were combined with biotin-modified Icaritin (ICT-Biotin, ICTBio) to synthesize Icaritin (ICT)-loaded black phosphorus nanosheets (BPICT). BPICT was then coated with macrophage membranes (MMs) to obtain MMs-camouflaged BPICT (M@BPICT). Herein, MMs allowed BPICT to target bone defects area, and BPICT accelerated the release of phosphate ions (PO43-) and ICT when exposed to NIR irradiation. PO43- recruited calcium ions (Ca2+) from the microenvironment to produce Ca3(PO4)2, and ICT increased the expression of osteogenesis-related proteins. Additionally, M@BPICT can decrease M1 polarization of macrophage and expression of pro-inflammatory factors to promote osteogenesis. According to the results, M@BPICT provided bone growth factor and bone repair material, modulated inflammatory microenvironment, and activated osteogenesis-related signaling pathways to promote bone regeneration. PTT could significantly enhance these effects. This strategy not only offers a solution to the challenging problem of drug-targeted delivery in bone defects but also expands the biomedical applications of MMs-camouflaged nanocarriers.

A Deep-Learning Model for Predicting the Efficacy of Non-vascularized Fibular Grafting Using Digital Radiography.

RATIONALE AND OBJECTIVES: To develop a fully automated deep-learning (DL) model using digital radiography (DR) with relatively high accuracy for predicting the efficacy of non-vascularized fibular grafting (NVFG) and identifying suitable patients for this procedure. MATERIALS AND METHODS: A retrospective analysis was conducted on osteonecrosis of femoral head patients who underwent NVFG between June 2009 and June 2021. All patients underwent standard preoperative anteroposterior (AP) and frog-lateral (FL) DR. Subsequently, the radiographs were pre-processed and labeled based on the follow-up results. The dataset was randomly divided into training and testing datasets. The DL-based prediction model was developed in the training dataset and its diagnostic performance was evaluated using the testing dataset. RESULTS: A total of 339 patients with 432 hips were included in this study, with a hip preservation success rate of 71.52% as of June 2023. The hips were randomly divided into a training dataset (n = 324) and a testing dataset (n = 108). The ensemble model in predicting the efficacy of NVFG, reaching an accuracy of 78.9%, a precision of 78.7%, a recall of 96.0%, a F1-score of 86.5%, and an area under the curve (AUC) of 0.780. FL views (AUC, 0.71) exhibited better performance compared to AP views (AUC, 0.66). CONCLUSION: The proposed DL model using DR enables automatic and efficient prediction of NVFG efficacy without additional clinical and financial burden. It can be seamlessly integrated into various clinical scenarios, serving as a practical tool to identify suitable patients for NVFG.

Sclerosis rim volume ratio and Japanese Investigation Committee classification as predictive factors for femoral head collapse progression after non-vascularized fibular grafting in osteonecrosis patients.

OBJECTIVE: The purpose of this retrospective cohort study was to determine the relationship between sclerosis rim volume ratio (SVR) and the progression of femoral head collapse after non-vascularized fibular grafting (NVFG) surgery in patients with osteonecrosis of the femoral head (ONFH), investigating risk factors associated with femoral head collapse progression and establishing a predictive model to enhance clinical decision-making. METHODS: ONFH patients who underwent NVFG between January 2008 and December 2021 were analyzed retrospectively to assess the risk of post-operative collapse progression (collapse >2 mm). A logistic regression model was used to evaluate the independent risk factors associated with collapse progression, including age, sex, etiology, affected side, Japanese Investigation Committee classification (JIC), and the sclerosis rim volume ratio (SVR). SVR values was collected from three weight-bearing columns, namely SVR1, SVR2, and SVR3, respectively. RESULTS: 57 patients with 64 hips who had undergone NVFG and were followed up for at least one year were included. During the follow-up, collapse＞2 mm occurred in 30 hips (46.88%). Multivariable analysis revealed that JIC (p =0.037) and SVR1 (p = 0.04) were independent risk factors for collapse progression after NVFG. The results of the receiver operating characteristic (ROC) analysis indicated that the aforementioned indices provided a satisfactory prediction of early femoral head collapse progression in ONFH patients after NVFG. The regression model using the above two indicators as a composite index showed satisfactory performance in predicting early postoperative femoral head collapse progression, with an area under the curve (AUC) of 84.6%. CONCLUSIONS: SVR is significant predictor of post-operative collapse progression following NVFG, and the composite index provides an optimal predictive value for femoral head collapse progression after surgery.

Predicting efficacy and guiding procedure choice in non-vascularized bone grafting: a CT Radiomics and clinical predictor approach.

OBJECTIVES: There is no practical approach for accurately predicting the efficacy of non-vascularized bone grafting (NVBG) and guiding its optimal procedure. MATERIALS AND METHODS: This study enrolled 153 patients with 182 hips that underwent NVBG procedures. The patients were randomly divided into a training cohort (n = 130) and a validation cohort (n = 52). In the training cohort, radiomics model, clinical model, and combined radiomics-clinical (C-R) model were constructed using Rad-scores and clinical predictors to predict the efficacy of NVBG. The optimal model was visualized by a nomogram and assessed by decision curve analysis (DCA). 128 hips that underwent successful NVBG were then randomized into a new training cohort (n = 92) and a new validation cohort (n = 36), and three models were constructed and validated to predict the choice of NVBG procedure. RESULTS: Japanese Investigation Committee (JIC) classification, exposure to risk factors postoperative, and Rad-scores consisting of four radiomics features were independent predictors for the efficacy of NVBG (P < 0.05). The C-R model provided better performance in both the training cohort (AUC: 0.818) and validation cohort (AUC: 0.747). To predict the choice of NVBG procedure, the C-R model built by JIC classification and Rad-scores consisting of five radiomics features showed the finest performance in both cohorts (AUC: 0.860 and 0.800, respectively). DCA showed great benefit using the C-R model for the choice of NVBG procedure. CONCLUSION: The approach integrated by CT radiomics and clinical predictors can be visually and quantitatively applied to predict the efficacy and guide the choice of NVBG procedure with great predictive accuracy.

Predictive value of clinical features and CT radiomics in the efficacy of hip preservation surgery with fibula allograft.

BACKGROUND: Despite being an effective treatment for osteonecrosis of the femoral head (ONFH), hip preservation surgery with fibula allograft (HPS&FA) still experiences numerous failures. Developing a prediction model based on clinical and radiomics predictors holds promise for addressing this issue. METHODS: This study included 112 ONFH patients who underwent HPS&FA and were randomly divided into training and validation cohorts. Clinical data were collected, and clinically significant predictors were identified using univariate and multivariate analyses to develop a clinical prediction model (CPM). Simultaneously, the least absolute shrinkage and selection operator method was employed to select optimal radiomics features from preoperative hip computed tomography images, forming a radiomics prediction model (RPM). Furthermore, to enhance prediction accuracy, a clinical-radiomics prediction model (CRPM) was constructed by integrating all predictors. The predictive performance of the models was evaluated using receiver operating characteristic curve (ROC), area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis. RESULTS: Age, Japanese Investigation Committee classification, postoperative use of glucocorticoids or alcohol, and non-weightbearing time were identified as clinical predictors. The AUC of the ROC curve for the CPM was 0.847 in the training cohort and 0.762 in the validation cohort. After incorporating radiomics features, the CRPM showed improved AUC values of 0.875 in the training cohort and 0.918 in the validation cohort. Decision curves demonstrated that the CRPM yielded greater medical benefit across most risk thresholds. CONCLUSION: The CRPM serves as an efficient prediction model for assessing HPS&FA efficacy and holds potential as a personalized perioperative intervention tool to enhance HPS&FA success rates.

[Visual and quantitative assessment of the effectiveness of non-vascularized bone grafting in osteonecrosis of the femoral head via CT-based radiomics and clinical data].

Objective: To investigate the value of CT-based radiomics and clinical data in predicting the efficacy of non-vascularized bone grafting (NVBG) in hip preservation, and to construct a visual, quantifiable, and effective method for decision-making of hip preservation. Methods: Between June 2009 and June 2019, 153 patients (182 hips) with osteonecrosis of the femoral head (ONFH) who underwent NVBG for hip preservation were included, and the training and testing sets were divided in a 7∶3 ratio to define hip preservation success or failure according to the 3-year postoperative follow-up. The radiomic features of the region of interest in the CT images were extracted, and the radiomics-scores were calculated by the linear weighting and coefficients of the radiomic features after dimensionality reduction. The clinical predictors were screened using univariate and multivariate Cox regression analysis. The radiomics model, clinical model, and clinical-radiomics (C-R) model were constructed respectively. Their predictive performance for the efficacy of hip preservation was compared in the training and testing sets, with evaluation indexes including area under the curve, C-Index, sensitivity, specificity, and calibration curve, etc. The best model was visualised using nomogram, and its clinical utility was assessed by decision curves. Results: At the 3-year postoperative follow-up, the cumulative survival rate of hip preservation was 70.33%. Continued exposure to risk factors postoperative and Japanese Investigation Committee (JIC) staging were clinical predictors of the efficacy of hip preservation, and 13 radiomic features derived from least absolute shrinkage and selection operator downscaling were used to calculate Rad-scores. The C-R model outperformed both the clinical and radiomics models in predicting the efficacy of hip preservation 1, 2, 3 years postoperative in both the training and testing sets ( P<0.05), with good agreement between the predicted and observed values. A nomogram constructed based on the C-R model showed that patients with lower Rad-scores, no further postoperative exposure to risk factors, and B or C1 types of JIC staging had a higher probability of femoral survival at 1, 2, 3 years postoperatively. The decision curve analysis showed that the C-R model had a higher total net benefit than both the clinical and radiomics models with a single predictor, and it could bring more net benefit to patients within a larger probability threshold. Conclusion: The prediction model and nomogram constructed by CT-based radiomics combined with clinical data is a visual, quantifiable, and effective method for decision-making of hip preservation, which can predict the efficacy of NVBG before surgery and has a high value of clinical application.

Prognosis and risk prediction of bone impaction grafting through femoral head-neck fenestration: a retrospective cohort study.

The bone impaction grafting through femoral head-neck fenestration was a favorable hip preservation procedure but without prognosis estimation. This study retrospectively reviewed 79 patients' clinical data (114 hips) with osteonecrosis of the femoral head (ONFH) who underwent this procedure from June 2009 to June 2019. By the end of June 2022, the median survival time of the hip was (74.13 ± 44.88) months, and the success rate of hip preservation was 68.42%. Lateral reserved angle (LPA) and combined reserved angle (CPA) had statistically significant differences (P < 0.001) both in univariate analysis and a multivariate logistic regression model. The multivariate logistic regression model of area under curve (AUC) area of the receiver operating characteristic (ROC) curve was 0.931(sensitivity = 95.00%, specificity = 88.40%, log-rank test: P < 0.01), and the calibration curve indicated good prediction accuracy. The ROC analysis and Cox proportional hazards regression model revealed that the cutoff point of LPA was 50.95° (sensitivity = 95.00%, specificity = 72.09%, log-rank test: P < 0.05) and the cutoff point of CPA was 90.51° (sensitivity = 90.00%, specificity = 90.70%, log-rank test: P < 0.05). A nomogram plot to predict the risk of failure (C-index = 0.873, 95% CI: 0.785 to 0.961) and nomograms for predicting the survival probability at 1, 2 or 3 years whose calibration curves showed excellent prediction accuracy were available for the clinician. Preserved angles (PAs) are valuable in the prediction of prognosis in surgical treatment. The bone impaction grafting through femoral head-neck fenestration can achieve better clinical efficacy, especially for patients with LPA >50.95° and CPA >90.51°.